In this work, we study the problem of single-channel mixed speech recognition using deep neural networks (DNNs). Using a multi-style training strategy on artificially mixed speech data, we investigate several different training setups that enable the DNN to generalize to corresponding similar patterns in the test data. We also introduce a WFST-based two-talker decoder to work with the trained DNNs. Experiments on the 2006 speech separation and recognition challenge task demonstrate that the proposed DNN-based system has remarkable noise robustness to the interference of a competing speaker. The best setup of our proposed systems achieves an overall WER of 19.7% which improves upon the results obtained by the state-of-the-art IBM superhuman system by 1.9% absolute, with fewer assumptions and lower computational complexity.
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